Technical Field
The present invention relates to techniques for automatically detecting a salient region in an image.
Related Art
Techniques are known for using image analysis to automatically detect an important region in an image, or a region likely to draw a person's focus (such regions are referred to as saliency regions below). These kinds of techniques are referred to as saliency detection or visual attention detection and have been increasingly gaining attention as an important technological feature in computer vision.
An algorithm for saliency detection can usually be classified roughly into a local technique and a global technique. A local technique calculates a saliency measure using the features (e.g. local contrast, edge orientation, and the like) extracted from a local region within the image as a clue (e.g., L. Itti, et. al., “A model of saliency-based visual attention for rapid scene analysis”, PAMI, 20(11):1254-1259, 1998; Non-Patent Document 1). On the other hand a global technique calculates a saliency measure using the features (e.g., global contrast, histograms) or prior knowledge (e.g., the background, or an easy to focus location) as clues (e.g., M. M. Cheng, et. al., “Global contrast based salient region detection”, In CVPR, pages 409-416, 2011; Non-Patent Document 2).
Many of these kinds of saliency detection algorithms have been proposed thus far. However, it tends to be difficult to obtain highly accurate detection results from a variety of images, and further improvement in the versatility and reliability of saliency detection is desired.    Non-Patent Document 1: L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. PAMI, 20(11):1254-1259, 1998.    Non-Patent Document 2: M. M. Cheng, G. X. Zhang, N. J. Mitra, X. Huang, and S. M. Hu. Global contrast based salient region detection. In CVPR, pages 409-416, 2011.    Non-Patent Document 3: P. Krahenbuhl and V. Koltun. Geodesic object proposals. In ECCV, pages 725-739. 2014.    Non-Patent Document 4: A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, pages 1097-1105, 2012.